is a well-known acceleration technique for CNN inference phase which has been widely researched and achieved outstanding advances.
Pruning of weights can be divided into five categories [1]}: element-level [2]}, vector-level [3]}, kernel-level [4]}, group-level [5]} and filter-level pruning [6]}[7]} [8]}[9]}[10]}. Weight pruning focuses on raising parameters sparsity of convolutional layers.
